Automatic systems purporting to recognize cursive script writing, or even handwritten characters, have so far met with only limited success. The reason for that can be traced largely to the lack of robustness exhibited by the templates used in the modeling of handwriting. For example, reference is made to U.S. Pat. No. 4,731,857 to Tappert which describes an elastic matching approach for the recognition of run-on handwritten characters.
Tappert teaches three steps. First, potential segmentation points are derived. Second, all combinations of the segments that could reasonably be a character are sent to a character recognizer to obtain ranked choices and corresponding scores. Third, the character sequences are combined so that the best candidate word wins.
Tappert's recognition algorithm itself is a template matching algorithm based on dynamic programming. Each template is a fully formed character presumably representative of the writer's average way of forming this character, and the elastic matching scores of the current character are computed for each template. This strategy is vulnerable to the extensive variability that can be observed both across writers and across time.
In an article entitled "Design of a Neural Network Character Recognizer for a Touch Terminal" by Guyon et al. (Pattern Recognition), a neural network is employed to classify (and thereby recognize) input characters. This results in a relatively robust algorithm but requires a large amount of data and is expensive to train.
A prior patent application entitled, "A Statistical Mixture Approach To Automatic Handwriting Recognition," filed by Bellegarda et al., application Ser. No. 07/785,642, now U.S. Pat. No. 5,343,537 is directed to a fast algorithm for handwriting recognition having an acceptable degree of robustness. Bellegarda's prior application application Ser. No. 07/785,642, now U.S. Pat. No. 5,343,537 entails at least three crucial specifications: (i) the feature elements should be chosen such as to characterize handwriting produced in a discrete, run-on, cursive, or unconstrained mode equally well; (ii) these feature elements should be suitably processed so as to minimize redundancy and thereby maximize the information represented on a per-parameter basis; and (iii) the resulting feature parameters should be further analyzed to detect broad trends in handwriting and enable appropriate modeling of these trends. These specifications are not met by the elastic matching approach taught by Tappert, since (i) it is character-based, and (ii) it simply averages several instances of a character to obtain a character template.
According to Bellegarda's prior application (application Ser. No. 07/785,642, now U.S. Pat. No. 5,343,537), the signal processing front-end is a great deal more sophisticated than that of elastic matching. Rather than merely chopping the input data into segments, the signal is transformed onto a higher dimensional feature space (chirographic space), whose points represent all raw observations after non-redundant feature extraction. Using a Gaussian (as opposed to Euclidean) measure for a more refined clustering, the prototypes in this space are formed for robustness purposes. Hence, each prototype represents a small building block which may be common to many characters. Instead of character sequences, building block sequences are combined, each of which is assigned a true likelihood defined on a bona fide probability space (as opposed to just a distance score). Finally, the recognition algorithm itself is a maximum a posteriori decoder operating on this probability space.
The formulation described in Bellegarda's prior application Ser. No. 07/785,642, now U.S. Pat. No. 5,343,537 may be alternatively cast in terms of multi-arc, single state, hidden Markov models. This formulation, while being robust, may not adequately model the intra-character variation of the alphabet.